X-ray multigrain crystallography

ABSTRACT

Disclosed is method of determining one or more unit cells of a poly-crystalline sample and indexing a set DV of 3D diffraction vectors . The method comprising obtaining a plurality of candidate first lattice plane normal vectors and a plurality of candidate second lattice plane normal vectors for a particular unknown grain; using said plurality of candidate first lattice plane normal vectors and said plurality of candidate second lattice plane normal vectors to select a plurality of subsets SSDV_n of the set DV of 3D diffraction vectors and processing said plurality of subsets SSDV_n of 3D diffraction vectors to determine a primary candidate unit cell PCUC defined by three lattice vectors; wherein the primary candidate unit cell PCUC is validated by evaluating the fit of the PCUC with the full set DV of 3D diffraction vectors.

FIELD

The present invention relates to X-ray multigrain crystallography,particular to a method of determining one or more unit cells of apoly-crystalline sample and indexing 3D diffraction vectors, to a methodof indexing diffraction spots, to a method of determining thecomposition of a poly-crystalline material, and to an X-ray diffractionapparatus.

BACKGROUND

X-ray crystallography is an important tool for analysing crystals andhas a central role in technical fields such as mineralogy, metallurgy,biology, and pharmacology. Traditionally X-ray crystallography has beenbased on either single crystals or powders. Single crystal X-raycrystallography provide superior information compared to powder X-raycrystallography but require larger crystals. Thus, a significant amountof work has to be performed in e.g. pharmacology for growing mono graincrystallites of a size and quality required for X-ray crystallographyanalysis.

X-ray multigrain crystallography is a relatively new approach [1] andcomplementary to traditional crystallographic analysis which is based oneither single crystals or powders. The experimental set-up is in thesimplest case identical to that typically used in single crystal X-raycrystallography, with a monochromatic beam, a fully illuminated samplein transmission geometry on a rotary table and a 2D detector. The imagesacquired during a rotation of the sample may comprise up to a milliondiffraction spots from the grains simultaneously illuminated. A key stepin the analysis of such data is multigrain indexing, i.e. associatingdiffraction spots with their crystal of origin.

Traditionally multigrain indexing requires a priori knowledge of thesample material and structure. At least the space group and the unitcells of the grains need to be known a priori in prior art indexingmethods. The unit cell corresponds to the physical crystal lattice of aparticular unknown grain and is defined by three lattice vectors. Hence,a brute force search and optimization procedure in the 9D space, spannedby these three lattice vectors, will provide an indexing of all grains.However, to our knowledge, such an approach is computationally notfeasible.

Thus, it remains a problem to provide a more computational efficientmethod for indexing grains.

SUMMARY OF THE INVENTION

According to a first aspect the present invention relates to a method ofdetermining one or more unit cells of a poly-crystalline sample andindexing a set DV of 3D diffraction vectors obtained by illuminatingsaid poly-crystalline sample with an X-ray source at one or moreorientations and recording diffraction spots using a at least one 2D or3D X-ray detector for each of said one or more orientations, said set DVof 3D diffraction vectors being indexed into one or more grains, saidmethod comprising the steps of:

(A) obtaining a plurality of candidate first lattice plane normalvectors and a plurality of candidate second lattice plane normal vectorsfor a particular unknown grain; using said plurality of candidate firstlattice plane normal vectors and said plurality of candidate secondlattice plane normal vectors to select a plurality of subsets SSDV_n ofthe set DV of 3D diffraction vectors and processing said plurality ofsubsets SSDV_n of 3D diffraction vectors to determine a primarycandidate unit cell PCUC defined by three lattice vectors; wherein thecorrespondence of the primary candidate unit cell PCUC is validated byevaluating the fit of the PCUC with the full set DV of 3D diffractionvectors; and

(B) determining if the fit of primary candidate unit cell PCUC with thefull set DV of 3D diffraction vectors is above a first threshold;

wherein if the fit of the primary candidate unit cell PCUC is above saidfirst threshold the primary candidate unit cell PCUC is used to identifya subset ST of the set DV of 3D diffraction vectors originating from asingle grain in the poly-crystalline sample, said subset ST is indexedwherein the method returns to step (A) unless a predetermined criteriahas been reached.

Consequently, by using candidate lattice plane normal vectors to reducethe full set DV of 3D diffraction vectors and using said reduced set tofind a primary candidate unit cell PCUC a more computational efficientmethod of indexing diffraction vectors is provided.

Furthermore, by validating the primary candidate unit cell PCUC on thefull set DV of diffraction vectors the indexing may be done with highaccuracy.

The X-ray source may preferably be a monochromatic source and the atleast one X-ray detector may preferably be a 2D detector. However, theX-ray source may also be a polychromatic source, and the at least oneX-ray detector may be a 3D detector capable of separating differentX-ray energies.

The measurement setup may be similar to the measurement setup used insingle crystal X-ray crystallography, see FIGS. 10 ab for details of howdiffraction spots may be recorded and transformed into diffractionvectors. The candidate first and second lattice plane normal vectors maybe generated using a pseudo random number generator (PRNG).Alternatively they may be selected from a pre-generated list.

The polycrystalline sample may be any material sample that contains atleast one crystal or a multiple of crystals, it may contain multiplephases, may be in a solid or powder form, may be a mixture, may containnon-crystalline material, such as a matrix or a liquid.

The predetermined criteria may be that the method determines that allindexable grains in the poly-crystalline sample have been indexed, thata predetermined number of grains have been indexed, that a predeterminedpercentage of the diffraction vectors has been indexed, that theremaining diffraction vectors is below a predetermined number, or thatstep (A) and (B) have been repeated a predetermined number of timeswithout identifying any new grains.

The method of the present invention provides indexed candidate grains,and further analysis and processing may be performed to improve orprovide a reconstructed model of the sample. The known tools used insingle crystal crystallography may be applied for such further analysisand processing.

The primary candidate unit cell may be either smaller or larger than thereal unit cell of the grain or it may be given by a non-standard set oflattice vectors. Thus the primary candidate unit may be furtheroptimized before it is used to index diffraction vectors e.g. bydetermining a standard reduced unit cell e.g. using methods disclosed in[2] or [3].

In some embodiments, the subset ST is removed from the set DV of 3Ddiffraction vectors before the method returns step (A).

In some embodiments, step (A) comprises the steps of:

(a) selecting a set L1 of candidate first lattice plane normal vectors;

(b) evaluating the fit of each candidate first lattice plane normalvector of the set L1 with the set DV of 3D diffraction vectors;

(c) selecting a subset SL1 of the set L1 comprising the N1 candidatefirst lattice plane normal vectors having the best fit with the set DVof 3D diffraction vectors;

(d) performing steps (d_a) to (d_g) for each candidate first latticeplane normal vector n of the subset SL1:

(d_a) using said candidate first lattice plane normal vector n to selecta subset SDV_n of the set DV of 3D diffraction vectors;

(d_b) selecting a set L2_n of candidate second lattice plane normalvectors;

(d_c) evaluating the fit of each candidate second lattice plane normalvector of the set L2_n with the subset SDV_n of 3D diffraction vectors;

(d_d) selecting a subset SL2_n of the set L2_n comprising the N2candidate second lattice plane normal vectors having the best fit withthe subset SDV_n of 3D diffraction vectors;

(d_e) using said subset SL2_n of candidate second lattice plane normalvectors to select a subset SSDV_n of the subset SDV_n of 3D diffractionvectors;

(d_f) processing said subset SSDV_n of 3D diffraction vectors todetermine a candidate unit cell CUC_n defined by three lattice vectors;

(d_g) evaluating the fit of the candidate unit cell CUC_n with the setDV of 3D diffraction vectors;

wherein the candidate unit cell of the N1 candidate unit cells CUC_n (n=1 to N1) having the best fit with the full set DV of 3D diffractionvectors is selected as the primary candidate unit cell PCUC.

Consequently, by evaluating the fit of a plurality of candidate firstlattice plane normal vectors with the set DV of 3D diffraction vectors,no a priori knowledge is needed to identify true candidates as a baseline for untrue candidate first lattice plane normal vectors mayautomatically be generated. Similar, by finding a plurality of candidateunit cells a base line for untrue candidate unit cells may be generated.Furthermore, by reducing the set DV of diffraction vectors before thesearch for a true second lattice plane normal vector is initiated(resulting in the subset SDV_n), and again before the candidate unitcell CUC_n is found (resulting in the set SSDV_n), the speed of themethod is further improved.

The set L1 may comprise at least 20, 100, 1000 or 10000 candidate firstlattice plane normal vectors. The set L2_n may comprise at least 20,100, 1000, or 10000 candidate second lattice plane normal vectors. Thesets L1 and L2_n may be generated using a PRNG. Alternatively the setsmay be selected from a pre-generated list. N1 may be at least 2, 5 or10.

In some embodiments, step (b) comprises the steps of:

(b1) performing steps (b1_a) and (b1_b) for each j candidate firstlattice plane normal vector of the set L1:

(b1_a) projecting the set DV of 3D diffraction vectors onto said jcandidate first lattice plane normal vector resulting in a set DV1D_j ofprojected 1D diffraction vectors;

(b1_b) filtering the set DV1D_j of projected 1D diffraction vectors witha plurality of filters comprising a regular array of box functions, thewidth of the boxes being the same for all filters but the distance ‘d’between the boxes is increased from a value d_min to a value d_max(preferably in increasingly larger steps), the output of each filteringprocess K is the normalised number of projected 1D diffraction vectorsthat are positioned within a box function, wherein the highest K valueis used as a measure of the fit of the n'th candidate first latticeplane normal vector with the set DV of 3D diffraction vectors; andwherein step (c) comprises selecting the N1 candidate first latticeplane normal vectors having the highest K value.

Consequently, an efficient method of identifying potential truecandidate 5 first lattice plane normal vectors is provided. By reducingthe 3D data set DV to a 1D dataset DV1D_j before the filtering process,the complexity is reduced.

The d_min value may be a value>q_min and the d_max value may be given byq_max/2, where q_min is the length of the smallest possible diffractionvector and q_max is the length of largest possible diffraction vectorq_min may be selected to be the smallest possible value for any materialor if knowledge of the type of material of the sample is available, thatknowledge may be used to select q_min. Correspondingly, q_max may beselected to be the largest possible value for any material or ifknowledge of the type of material of the sample is available, thatknowledge may be used to select q_max.

The width of the boxes may be selected to be 2e where e is theexperimentally established center-of-mass errors of the position of thereflections in reciprocal space

The output K is normalized to take account of the diffraction vectorsthat by chance are positioned within a box. This may be done bysubtracting from the count a number given by equation 1 below:

$\begin{matrix}{\frac{w}{d} \cdot {N\_ DV}} & (1)\end{matrix}$

where w is the width of the boxes, d is the distance between the centreof each box, and N_DV is the number of diffraction vectors in the setDV. Thus, highest K value should be close to zero for an untruecandidate first lattice plane normal vector.

In some embodiments, step (c) further comprises for each n candidatefirst lattice plane normal vector of the subset SL1, optimizing the ncandidate first lattice plane normal vector by performing a local gridsearch resulting in an subset SL1* of optimized candidate first latticeplane normal vectors, wherein the optimized subset SL1* is used in step(d).

In some embodiments, step (d_a) comprises selecting the 3D diffractionvectors of the set DV that when they are projected onto said n candidatefirst lattice plane normal vector are positioned within a box functionfor the filter having the highest K value.

For true candidate first lattice plane normal vectors this result in asubset that contains all or almost all of the diffraction vectors fromone particular grain but also a significant number of reflections forother grains that by chance happen to be projected such that they fallwithin the boxes.

In some embodiments, the step (d_c) comprises the steps of:

(d_c1) performing steps (d_c1_a) and (d_c1_b) for each k candidatesecond lattice plane normal vector of the set L2_n:

(d_c1_a) projecting the subset SDV_n of 3D diffraction vectors onto saidk candidate second lattice plane normal vector resulting in a setSDV_n1D_n of projected 1D diffraction vectors;

(d_c1_b) filtering the set SDV_n1D_k of projected 1D diffraction vectorswith a plurality of filters comprising a regular array of box functions,the width of the boxes being the same for all filter but the distance‘d’ between the boxes is increased from a value d_min to a value d_max(preferably in increasingly larger steps), the output of each filteringprocess K is the normalized number of projected 1D diffraction vectorsthat are positioned within a box function, wherein the highest K valueis used as a measure of the fit of the k'th candidate second latticeplane normal vector with the set SDV_n of 3D diffraction vectors; andwherein step (d_d) comprises selecting the N3 candidate second latticeplane normal vectors having the highest K value.

In some embodiments, step (d_d) further comprises for each k candidatesecond lattice plane normal vector of the subset SL2_n, optimizing the kcandidate second lattice plane normal vector by performing a local gridsearch resulting in an subset SL2_n* of optimized candidate secondlattice plane normal vectors, wherein the optimized subset SL2_n* isused in step (d_e).

Consequently, candidate second lattice plane normal vectors may moreprecisely correspond to true lattice plane normal vectors. This mayimprove the speed of the method and improve the precision of theindexing.

In some embodiments, the candidate second lattice plane normal vectorCL2_n of the subset SL2_n* having the best fit with the subset SDV_n of3D diffraction vectors is used to select the subset SSDV_n of the subsetSDV_n of 3D diffraction vectors.

In some embodiments, step (d_e) comprises selecting the 3D diffractionvectors of the subset SDV_n that when they are projected onto CL2_n arepositioned within a box function for the filter having the highest Kvalue.

In some embodiments, step (d_f) comprises the steps of:

(d_f_a) selecting a subset SSSDV_n of the subset SSDV_n of 3Ddiffraction vectors;

(d_f_b) determining the normal vector for all planes spanned by threediffraction vectors of the subset SSSDV_n of 3D diffraction vectors(ignoring sets of three diffraction vectors having a cross product closeto zero) each normal vector being a candidate lattice plane normalvector thereby resulting in a set NL_n of new candidate lattice planenormal vectors;

(d_f_c) evaluating the fit of each new candidate lattice plane normalvector of the set NL_n with the subset SSDV_n of 3D diffraction vectors;and

(d_f_d) determining a candidate unit cell CUC_n.

Consequently, by finding new candidate lattice plane normal vectors therisk of choosing an ambiguous candidate unit cell is lowered.

The subset SSSDV_n may be the X shortest diffraction vectors of thesubset SSDV_n. X may be any number between 5 and 500, 10, and 250, or 20and 100 e.g. the 50 shortest diffraction vectors.

In some embodiments, step (d_f_c) comprises:

(d_f_c1) performing steps (d_f_c1_a) and (d_f_c1_b) for each i newcandidate lattice plane normal vector of the set NL_n:

(d_f_c1_a) projecting the subset SSDV_n of 3D diffraction vectors ontosaid i new candidate lattice plane normal vector resulting in a setSSDV_n_1D_i of 1D diffraction vectors;

(d_f_c1_b) filtering the set SSDV_n_1D_i of projected 1D diffractionvectors with a plurality of filters comprising a regular array of boxfunctions, the width of the boxes being the same for all filter but thedistance ‘d’ between the boxes is increased between a value d_min and avalue d_max, the output of each filtering process K is the number ofprojected 1D diffraction vectors that are positioned within a boxfunction, wherein the K value for the filter having the largest distance‘d*’ between the boxes while maintaining a K value being at least X % ofthe maximum K value for all filters is selected as a measure F of thefit of the i'th new candidate lattice plane normal vector with thesubset SSDV_n of 3D diffraction vectors, and wherein the vector pointingalong the i'th new candidate lattice plane normal vector and having thelength 1/′d*′ is selected as a candidate direct-lattice vector whereby aset CDLV_n of candidate direct-lattice vectors is created, and

wherein step (d_f_d) comprises:

selecting three linear independent candidate direct-lattice vectors ofthe set CDLV_n based on their F values as the candidate unit cell CUC_n.

X may be any percentage between 50% and 100% or 70% and 90% e.g. 80%.

In some embodiments, step (d_f_d) comprises:

removing all candidate direct-lattice vectors from the set CDLV_n havinga F value lower than Y % of the maximum F value of the set CDLV_n andselecting the three shortest linear independent candidate direct-latticevectors remaining in the set CDLV_n as the candidate unit cell CUC_n.

Y may be any percentage between 50% and 100% or 70% and 90% e.g. 80%.

In some embodiments, step (d_g) comprises determining the reciprocallattice vectors for the candidate unit cel CUC_n and finding the numberP of 3D diffraction vectors of the set DV being within a predetermineddistance M from each reciprocal lattice point wherein said number P is ameasure of the fit of the CUC_n with the set DV of 3D diffractionvectors.

In some embodiments, the subset ST of the set DV of 3D diffractionvectors originating from a single grain in the poly-crystal sample isindentified by:

determining the reciprocal lattice vectors for the primary candidateunit cell PCUC and selecting the 3D diffraction vectors of the set DVbeing within a predetermined distance M from each reciprocal latticepoint.

M may be a number between 1e and 10e, between 3e and 7e e.g. 5e, where eis the experimental center-of-mass errors of the position of thereflections in sample space.

According to a second aspect the present invention relates to a methodof indexing a plurality of diffraction spots, said method comprising thesteps of:

indexing the diffraction vectors corresponding to the plurality ofdiffraction spots using a method as disclosed in relation to the firstaspect of the present invention; and

using said indexed diffraction vectors to index said correspondingdiffraction spots.

According to a third aspect the present invention relates to a method ofdetermining the composition of a poly-crystalline sample comprising thesteps of:

-   -   illuminating a poly-crystalline sample with an X-ray source at a        plurality of orientations and recording diffraction spots using        at least one 2D or 3D X-ray detector for each of said plurality        of orientations;    -   transforming said recorded diffraction spots into a set DV of        diffraction vectors;    -   indexing said set DV of diffraction vectors using a method as        disclosed in relation to the first aspect of the present        invention;    -   processing said indexed diffraction vectors to determine the        composition of the poly-crystalline sample.

According to a fourth aspect, the present invention relates to acomputer program product comprising program code means adapted to causea data processing system to perform the steps of the method as disclosedin relation the any one of the embodiments of the first aspect of thepresent invention, when said program code means are executed on the dataprocessing system.

In some embodiments, said computer program product comprises anon-transitory computer-readable medium having stored thereon theprogram code means.

According to a fifth aspect, the present invention relates to a dataprocessing system configured to perform the method as disclosed inrelation the any one of the embodiments of the first aspect of thepresent invention

According to a sixth aspect, the present invention relates to an X-raydiffraction apparatus for analysing a poly-crystalline sample,comprising:

an X-ray source for emitting an X-ray beam;

a staging device for positioning the poly-crystalline sample in the beampath in a plurality of mutually different angular positions relative tothe to the X-ray source;

an X-ray detector for detecting diffracted X-ray beams leaving thepoly-crystalline sample; and

a processing unit operationally connected to said X-ray detector saidprocessing unit being configured to determine a set DV of 3D diffractionvectors by processing data received from said X-ray detector obtained byilluminating the poly-crystalline sample at a plurality of orientations;and indexing said set DV of 3D diffraction vectors using a method asdisclosed in relation the any one of the embodiments of the first aspectof the present invention.

The staging device may be movable, i.e. the staging device may rotatethe sample. Alternatively/additionally the X-ray source and the X-raydetector may be movable, i.e. the X-ray source and the X-ray detectormay be configured to orbit the sample.

According to a seventh aspect the present invention relates to a methodof determining at least a first property of at least a first grain of apolycrystalline sample using at least one selected analysing methodselected from the group of analysing methods consisting of: a reciprocalspace mapping method, a defect studies method, and a method for findingthe solution and the refinement of the at least the first grain, saidmethod comprising the steps of:

-   -   illuminating said poly-crystalline sample with an X-ray source        at a plurality of orientations and recording diffraction spots        using a 2D or 3D X-ray detector for each of said plurality of        orientations;    -   transforming said recorded diffraction spots into a set DV of        diffraction vectors;    -   indexing said set DV of diffraction vectors using a method        according to any one of claims 1 to 10 to determine a subset of        said set DV of diffraction vectors originating from said first        grain; and

analysing the diffraction vectors and/or their corresponding diffractionspots originating from said first grain using said selected analysingmethod to determine said at least first property of said first grain.

Examples of a reciprocal space mapping method may be found in [4] [5].An example of a defect studies method may be found in [6]. Examples ofmethods for finding the solution and the refinement of a grain can befound in [7] [8].

Here and in the following, the term ‘processing unit’ is intended tocomprise any circuit and/or device suitably adapted to perform thefunctions described herein. In particular, the above term comprisesgeneral purpose or proprietary programmable microprocessors, DigitalSignal Processors (DSP), Application Specific Integrated Circuits(ASIC), Programmable Logic Arrays (PLA), Field Programmable Gate Arrays(FPGA), special-purpose electronic circuits, etc. or a combinationthereof

The different aspects of the present invention can be implemented indifferent ways as described above and in the following, each yieldingone or more of the benefits and advantages described in connection withat least one of the aspects described above, and each having one or morepreferred embodiments corresponding to the preferred embodimentsdescribed in connection with at least one of the aspects described aboveand/or disclosed in the dependant claims. Furthermore, it will beappreciated that embodiments described in connection with one of theaspects described herein may equally be applied to the other aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional objects, features and advantages of thepresent invention will be further elucidated by the followingillustrative and non-limiting detailed description of embodiments of thepresent invention, with reference to the appended drawings, wherein:

FIG. 1 shows a flowchart for a method of indexing a set DV of 3Ddiffraction vectors according to an embodiment of the present invention.

FIG. 2 show a flowchart for a first part of a method according to anembodiment of the present invention.

FIG. 3 show a flowchart for a second part of a method according to anembodiment of the present invention.

FIG. 4 show a flowchart for a third part of a method according to anembodiment of the present invention.

FIG. 5 show a flowchart for a fourth part of a method according to anembodiment of the present invention.

FIG. 6 show a flowchart for a fifth part of a method according to anembodiment of the present invention.

FIG. 7a-c illustrates schematically a filtering process according to anembodiment of the present invention.

FIG. 8 illustrates schematically a filtering process according to anembodiment of the present invention.

FIG. 9 shows a schematic drawing of an X-ray diffraction apparatusaccording to an embodiment of the present invention.

FIGS. 10a-b show schematically how diffraction spots may be recorded andtransformed into diffraction vectors.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingfigures, which show by way of illustration how the present invention maybe practiced.

FIG. 1 shows a flowchart for a method of determining one or more unitcells of a poly-crystalline sample and indexing a set DV of 3Ddiffraction vectors according to an embodiment of the present invention.The set DV of 3D diffraction vectors is obtained by illuminating thepoly-crystalline sample with an X-ray source at a plurality oforientations and recording diffraction spots using a 2D or 3D X-raydetector for each of the plurality of orientations. The set DV of 3Ddiffraction vectors is being indexed into a plurality of grains. Themethod starts in step 101 with obtaining a plurality of candidate firstlattice plane normal vector and a plurality of candidate second latticeplane normal vectors for a particular grain. The candidate first andsecond lattice plane normal vectors are 3D vectors that may be generatedby a pseudo random number generator (PRNG). Next, the plurality ofcandidate first lattice plane normal vectors and the plurality ofcandidate second lattice plane normal vectors are used to select aplurality of subsets SSDV_n (n=1 to N1) of the set DV of 3D diffractionvectors. These subsets SSDV_n are processed to determine a primarycandidate unit cell PCUC defined by three lattice vectors. The primarycandidate unit cell PCUC is validated by evaluating the fit of the PCUCwith the full set DV of 3D diffraction vectors. Next, the methodcontinues to step B 102 where it is determined if the fit of the primarycandidate unit cell PCUC with the full set DV of 3D diffraction vectorsis above a first threshold. If the fit is above the first threshold themethod continues to step 103 where the primary candidate unit cell PCUCis used to identify a subset ST of the set DV of 3D diffraction vectorsoriginating from a single grain. The subset ST is then indexed andremoved from the set DV of 3D diffraction vectors and the methodcontinues to step 104. Alternatively, if the fit is found to be belowthe first threshold the method skips step 103 and continues directlyfrom step 102 to step 104. In step 104 the method determines if a firstpredetermined criteria has been reached. Examples of a predeterminedcriteria are: that all indexable grains in the poly-crystal sample havebeen indexed, that a predetermined number of grains have been indexed,that a predetermined percentage of the diffraction vectors has beenindexed, that the remaining diffraction vectors is below a predeterminednumber, or that step (A) and (B) have been repeated a predeterminednumber of times without identifying any new grains. If the predeterminedcriteria has not yet been reached the method returns to step A to lookfor a new primary candidate unit cell PCUC. Alternatively, if thepredetermined criteria have been reached the method terminates in step105.

FIG. 2 shows a flowchart for a first part of a method according to anembodiment of the present invention. Specifically FIG. 2 shows how stepA in the method disclosed in relation to FIG. 1 may be carried out.Firstly, in step 201 a set L1 of candidate first lattice plane normalvectors are selected. The set L1 may be selected using a PRNG. Next, instep 202 the fit of each candidate first lattice plane normal vector ofthe set L1 with the set DV of 3D diffraction vectors is evaluated. Then,in step 203 a subset SL1 of the set L1 comprising the N1 candidate firstlattice plane normal vectors having the best fit with the set DV of 3Ddiffraction vectors is selected. Next, in step 204, for each individualcandidate first lattice plane normal vector of the subset SL1 steps205-211 are performed.

First, in step 205 the n'th (starting with n=1) candidate first latticeplane normal vector of the subset SL1 is used to select a subset SDV_nof the set DV of 3D diffraction vectors. Next, in step 206 a set L2_n ofcandidate second lattice plane normal vectors is selected. The set L2_nmay be selected using a PRNG. Then, in step 207 the fit of eachcandidate second lattice plane normal vector of the set L2_n with thesubset SDV_n of 3D diffraction vectors is evaluated. Next, in step 208 asubset SL2_n of the set L2_n is selected comprising the N2 candidatesecond lattice plane normal vectors having the best fit with the subsetSDV_n of 3D diffraction vectors. Then, in step 209 the subset SL2_n ofcandidate second lattice plane normal vectors is used to select a subsetSSDV_n of the subset SDV_n of 3D diffraction vectors. The subset SSDV_nof 3D diffraction vectors is then in step 210 processed to determine acandidate unit cell CUC_n defined by three lattice vectors, and finallyin step 211 the fit of the candidate unit cell CUC_n with the set fullset DV of 3D diffraction vectors is evaluated. The method then returnsto step 205 if n is below N1 and increases n by 1. Alternatively, if nis equal to N1 (meaning that steps 205-211 have been performed for eachcandidate first lattice plane normal vector of the subset SL1) themethod continues to step 212 where the candidate unit cell of the N1candidate unit cells CUC_n (n=1 to N1) having the best fit with the fullset DV of 3D diffraction vectors is selected as the primary candidateunit cell PCUC.

FIG. 3 shows a flowchart for a second part of a method according to anembodiment of the present invention. Specifically FIG. 3 shows anexample of how step 202 in the method disclosed in relation to FIG. 2may be carried out.

For each candidate first lattice plane normal vector of the set L1 steps301 and 302 are performed. First, in step 301 the set DV of 3Ddiffraction vectors is projected onto the j'th (starting with j=1)candidate first lattice plane normal vector resulting in a set DV1D_j ofprojected 1D diffraction vectors. Next, in step 302 the set DV1D_j ofprojected 1D diffraction vectors is filtered with a plurality of filterscomprising a regular array of box functions, the width of the boxesbeing the same for all filter but the distance ‘d’ between the boxes isincreased from a value d_min to a value d_max (in increasingly largersteps), the output of each filtering process K is the normalised numberof projected 1D diffraction vectors that are positioned within a boxfunction, wherein the highest K value is used as a measure of the fit ofthe j'th candidate first lattice plane normal vector with the set DV of3D diffraction vectors. The method then returns to step 301 if j isbelow N1 (N1 being equal to the number of candidate first lattice planenormal vectors in the set L1) and increases n by 1. Alternatively, if jis equal to N1 (meaning that steps 201 and 202 have been performed foreach candidate first lattice plane normal vector of the set L1) themethod continues to step 203 in FIG. 2.

FIGS. 7a-b illustrates schematically the filtering process. In thissimplified example only 23 diffraction vectors originating from a firstgrain and a second grain are present. However, normally the number ofdiffraction vectors and grains will be much larger. The terminal pointfor the projected 1D diffraction vectors originating from the firstgrain are shown as boxes 701 and the terminal point of the projected 1Ddiffractions vectors originating from the second grain are shown ascrosses 702. Thus, there are 9 projected 1D diffraction vectorsoriginating from the first grain and 14 originating from the secondgrain. The 23 3D diffraction vectors are in Figs. a-b projected onto acandidate lattice plane normal vector that is identical to one of thethree true lattice plane normal vectors for the first grain but notidentical to any one of the three true lattice plane normal vectors forthe second grain. Consequently, the projected 1D diffraction vectorsoriginating from the first grain are arranged with a regular spacing,and the projected 1D diffraction vectors originating from the secondgrain are arranged with an irregular spacing.

FIG. 7a illustrates a first filtering process. Shown is a regular arrayof box functions 703 having a width w and being arranged with a distance‘d_1’. The distance ‘d_1’ is measured between the centre of twoneighbouring box functions. For this filtering process the distance‘d_1’ between the boxes does not match the regular spacing between 1Ddiffraction vectors originating from the first grain. The number ofdiffraction vectors arranged within a box function is in this example12, to get output K of the filtering process this number is ‘normalized’by subtracting the expected number of diffraction vectors that by chancewill be arranged within a box function. Using equation (1) this numberis found to be 0.53*23=12,19. Thus the output K of the filtering processis −0.19.

FIG. 7b illustrates a second filtering process. Shown is a regular arrayof box functions 703 having a width w and being arranged with a distance‘d_2’. The width w is the same for both filtering process but ‘d_2’ islarer than ‘d_1’. For this filtering process the distance ‘d_2’ betweenthe boxes matches the regular spacing between 1D diffraction vectorsoriginating from the first grain. The number of diffraction vectorsarranged within a box function is in this example 13, to get the outputK of the filtering process this number is ‘normalized’ by subtractingthe expected number of diffraction vectors that by chance will bearranged within a box function. Again using equation (1) this number isfound to be 0.35*23=8. Thus the output K of the filtering process is 5.

FIG. 7c shows the 23 3D diffraction vectors projected onto a candidatelattice plane normal vector that is non-identical to any one of thethree true lattice plane normal vectors for both the first and thesecond grain. Consequently, the projected 1D diffraction vectorsoriginating from the both the first and the second grain are arrangedwith an irregular spacing.

As an alternative to the filtering process disclosed in relation to FIG.7 a method based on frequency analysis may be used to evaluate the fitof each candidate lattice plane normal vector. This may be done bysampling the project 1D diffraction vectors to create a sampled signal,and determining the amplitude spectrum for the sampled signal. Bystudying the amplitude spectrum in the frequency range between 1/d_maxto 1/d_min the fit of the candidate lattice plane normal vectors withthe set DV of 3D diffraction vectors may be determined e.g. by examiningthe peak amplitude within that frequency range.

FIG. 8 shows the subset of the projected 23 diffraction vectors shown inFIG. 7a-b that are positioned within a box function for the filterhaving the highest K value (the filter shown in FIG. 7b ).

FIG. 4 shows a flowchart for a third part of a method according to anembodiment of the present invention. Specifically FIG. 4 shows anexample of how step 207 in the method disclosed in relation to FIG. 2may be carried out. For each candidate second lattice plane normalvector of the set L2_n steps 401 and 402 are performed. First, in step401 the subset SDV_n of 3D diffraction vectors is projected onto the kcandidate second lattice plane normal vector resulting in a setSDV_n1D_n of projected 1D diffraction vectors. Then, the set SDV_n1D_kof projected 1D diffraction vectors is filtered with a plurality offilters comprising a regular array of box functions, the width of theboxes being the same for all filter but the distance ‘d’ between theboxes is increased from a value d_min to a value d_max (in increasinglylarger steps), the output of each filtering process K is the normalizednumber of projected 1D diffraction vectors that are positioned within abox function, wherein the highest K value is used as a measure of thefit of the k'th candidate second lattice plane normal vector with theset SDV_n of 3D diffraction vectors. The filtering process may beperformed using the same principles disclosed in relation to FIG. 7.

The method then returns to step 401 if k is below N2 (N2 being equal tothe number of candidate second lattice plane normal vectors in the setL2_n) and increases k by 1. Alternatively, if k is equal to N2 (meaningthat steps 301 and 302 have been performed for each candidate secondlattice plane normal vector of the set L2_n) the method continues tostep 208 in FIG. 2.

FIG. 5 shows a flowchart for a fourth part of a method according to anembodiment of the present invention. Specifically FIG. 5 shows anexample of how step 210 in the method disclosed in relation to FIG. 2may be carried out. The method starts in step 501 with selecting asubset SSSDV_n of the subset SSDV_n of 3D diffraction vectors. Thesubset SSSDV_n may comprise the 50 shortest diffraction vectors of thesubset SSDV_n of 3D diffraction vectors. The method then continues tostep 502 where the normal vector for all planes spanned by threediffraction vectors of the subset SSSDV_n of 3D diffraction vectors(ignoring sets of three diffraction vectors having a cross product closeto zero) are determined. Since each normal vector is a candidate latticeplane normal vector this result in a set NL_n of new candidate latticeplane normal vectors. Next, in step 503, the fit of each new candidatelattice plane normal vector of the set NL_n with the subset SSDV_n of 3Ddiffraction vectors is evaluated, and finally in step 504 a candidateunit cell CUC_n is determined. The method then continues to step 212 inFIG. 2.

FIG. 6 shows a flowchart for a fifth part of a method according to anembodiment of the present invention. Specifically FIG. 6 shows anexample of how steps 503 and 504 in the method disclosed in relation toFIG. 5 may be carried out.

For each new candidate lattice plane normal vector of the set NL_n steps601 and 602 are performed. First, in step 601 the subset SSDV_n of 3Ddiffraction vectors is projected onto the i'th (starting with i=1) newcandidate lattice plane normal vector resulting in a set SSDV_n_1D_i of1D diffraction vectors. Next in step 602, the set SSDV_n_1D_i ofprojected 1D diffraction vectors is filtered with a plurality of filterscomprising a regular array of box functions, the width of the boxesbeing the same for all filter but the distance ‘d’ between the boxes isincreased between a value d_min and a value d_max, the output of eachfiltering process K is the number of projected 1D diffraction vectorsthat are positioned within a box function, wherein the K value for thefilter having the largest distance ‘d*’ between the boxes whilemaintaining a K value being at least X % of the maximum K value for allfilters is selected as a measure F of the fit of the i'th new candidatelattice plane normal vector with the subset SSDV_n of 3D diffractionvectors, and wherein the vector pointing along the i'th new candidatelattice plane normal vector and having the length 1/‘d*’ is selected asa candidate direct-lattice vector. The method then returns to step 601if i is below N4 (N4 being equal to the number of new candidate latticeplane normal vector of the set NL_n) and increases i by 1.Alternatively, if i is equal to N4 (meaning that steps 601 and 602 havebeen performed for each new candidate lattice plane normal vector of theset NL_n) the method continues to step 603. This result in a set CDLV_nof candidate direct-lattice vectors comprising N4 candidatedirect-lattice vectors. In the 603, three linear independent candidatedirect-lattice vectors of the set CDLV_n is selected as the candidateunit cell CUC_n based on their respective F values.

FIG. 9 shows a schematic drawing of an X-ray diffraction apparatus 901for analysing a poly-crystalline sample according to an embodiment ofthe present invention. The X-ray diffraction apparatus comprises:

an X-ray source 902 for emitting an X-ray beam; a staging device 903 forpositioning the poly-crystalline sample in the beam path in a pluralityof mutually different angular positions in relation to the X-ray source902; an X-ray detector 904 for detecting at least diffracted X-ray beamsleaving the poly-crystalline sample; and a processing unit 905operationally connected to at least the X-ray detector 904. Theprocessing unit 905 is preferably also operationally connected to theX-ray source 902 and the staging device 903 enabling the processing unitto control them. The processing unit 905 is configured to determine aset DV of 3D diffraction vectors by processing data received from saidX-ray detector obtained by illuminating the poly-crystalline sample at aplurality of orientations; and

indexing the set DV of 3D diffraction vectors using a method asdisclosed in relation the any one of the embodiments of the first aspectof the present invention and or the embodiments disclosed in relation toany one of FIGS. 1 to 8.

FIGS. 10a-b show schematically how diffraction spots may be recorded andtransformed into diffraction vectors. The set of diffraction vectors canbe determined in a number of ways. The most commonly used is the“rotation method” known from single-crystal crystallography [9] in whichcase the sample to detector distance 1013 is sufficiently large that allgrains can be considered to be located at the origin of the sample (i.e.a far-field configuration). In such a setup a monochromatic x-ray beam1001 is illuminating the sample 1005 through an aperture 1002 and a beamstop 1004 and a detector 1007 is provided behind the sample 1005. Thesample 1005 is mounted on a rotation stage with one or more rotationalaxes 1003. In one embodiment, the sample is rotated around a single axis1003 perpendicular to the incoming beam as illustrated in FIG. 10 a. Asan option, x, y and z translations may be added, as well as additionalrotations. Any part of the illuminated structure which fulfills theBragg condition 1012 (e.g. a lattice plane) will generate a diffractedbeam 1006. Images are acquired from multiple rotational positions of thesample to ensure multiple diffraction spots 1008 from all illuminatedgrains. In this monochromatic far-field configuration the diffractionvectors 1011 are derived from knowledge of the incoming beam direction1009 and the directional vectors 1010 from the sample origin to themeasure diffraction spots on the detector(s) 1007 (See FIG. 10b ). Otherexamples of experimental methods for measuring diffractions vectorsinclude monochromatic near-field configurations such as those describedin [10], and [11], as well as polychromatic configurations (see e.g.[12] and [13]).

Although some embodiments have been described and shown in detail, thepresent invention is not restricted to them, but may also be embodied inother ways within the scope of the subject matter defined in thefollowing claims. In particular, it is to be understood that otherembodiments may be utilized and structural and functional modificationsmay be made without departing from the scope of the present invention.

In system claims enumerating several means, several of these means canbe embodied by one and the same item of hardware. The mere fact thatcertain measures are recited in mutually different dependent claims ordescribed in different embodiments does not indicate that a combinationof these measures cannot be used to advantage.

It should be emphasized that the term “comprises/comprising” when usedin this specification is taken to specify the presence of statedfeatures, integers, steps or components but does not preclude thepresence or addition of one or more other features, integers, steps,components or groups thereof.'

REFERENCES

-   [1] Sorensen et al. 2012b Z. Kristall. 227, 63-78-   [2] Gruber, B. (1973). Acta Cryst. A29, 433-440-   [3] Grosse-Kunstleve, R . W., Sauter, N. K. & Adams, P. D. (2004).    Acta Cryst. A60, 1-6.-   [4] Jakobsen, B., Poulsen, H. F., Lienert, U. & Pantleon, W. (2007).    Acta Mater. 55, 3421-3430-   [5] Wejdemann, C., Poulsen, H. F., Lienert, U. & Pantleon, W.    (2013). JOM. 65, 35-43-   [6] Ungar, T., Ribarik, G., Balogh, L., Salem, A. A.,    Semiatin, S. L. & Vaughan, G. B. M. (2010). Scripta Mater. 63, 69-72-   [7] Schmidt, S. (2014). J. Appl. Cryst. 47, 276-284-   [8] Sorensen, H. O., Schmidt, S., Wright, J. P., Vaughan, G. B. M.,    Techert, S., Garman, E. F., Oddershede, J., Davaasambu, J.,    Paithankar, K. S., Gundlach, C., & Poulsen H. F. (2012b). Z-   [9] C. Giacovazzo, H. L. Monaco, D. Viterbo, F. Scordari, G.    Gilli, G. Zanotti, M. Catti: Fundamentals of Crystallography, IUCr    Texts on Crystallography, No. 2 (Oxford University Press, Oxford    1992)-   [10] E. M. Lauridsen, S. Schmidt, R. M. Suter, H. F. Poulsen (2001),    Tracking: A method for structural characterization of grains in    powders or polycrystals, J. Appl. Cryst., Vol. 34, 744-750-   [11] Ludwig, W., P. Reischig, A. King, M. Herbig, E. M.    Lauridsen, G. Johnson, T. J. Marrow, J. Y. Buffiere;    Three-dimensional grain mapping by x-ray diffraction contrast    tomography and the use of Friedel pairs in diffraction data    analysis, Review of Scientific Intruments, 2009, 80(3):033905-   [12] Larson, B. C., Yang, W., Ice, G. E., Budai, J. D. &    Tischler, J. Z. (2002). Nature, 415, 887-890.-   [13] A. King, P. Reischig, J. Adrien, W. Ludwig, First laboratory    X-ray diffraction contrast tomography for grain mapping of    polycrystals, J. Appl. Cryst. (2013). 46, 1734-1740

1. A method of determining one or more unit cells of a poly-crystallinesample and indexing a set DV of 3D diffraction vectors obtained byilluminating said poly-crystalline sample with an X-ray source at one ormore orientations and recording diffraction spots using a at least one2D or 3D X-ray detector for each of said one or more orientations, saidset DV of 3D diffraction vectors being indexed into a plurality ofgrains, said method comprising the steps of: (A) obtaining a pluralityof candidate first lattice plane normal vectors and a plurality ofcandidate second lattice plane normal vectors for a particular unknowngrain; using said plurality of candidate first lattice plane normalvectors and said plurality of candidate second lattice plane normalvectors to select a plurality of subsets SSDV_n of the set DV of 3Ddiffraction vectors and processing said plurality of subsets SSDV_n of3D diffraction vectors to determine a primary candidate unit cell PCUCdefined by three lattice vectors; wherein the correspondence of theprimary candidate unit cell PCUC is validated by evaluating the fit ofthe PCUC with the full set DV of 3D diffraction vectors; and (B)determining if the fit of primary candidate unit cell PCUC with the fullset DV of 3D diffraction vectors is above a first threshold; wherein ifthe fit of the primary candidate unit cell PCUC is above said firstthreshold the primary candidate unit cell PCUC is used to identify asubset ST of the set DV of 3D diffraction vectors originating from asingle grain in the poly-crystalline sample, said subset ST is indexedwherein the method returns to step (A) unless a predetermined criteriahas been reached.
 2. A method according to claim 1, wherein, step (A)comprises the steps of: (a) selecting a set L1 of candidate firstlattice plane normal vectors; (b) evaluating the fit of each candidatefirst lattice plane normal vector of the set L1 with the set DV of 3Ddiffraction vectors; (c) selecting a subset SL1 of the set L1 comprisingthe N1 candidate first lattice plane normal vectors having the best fitwith the set DV of 3D diffraction vectors; (d) performing steps (d_a) to(d_g) for each candidate first lattice plane normal vector n of thesubset SL1: (d_a) using said candidate first lattice plane normal vectorn to select a subset SDV_n of the set DV of 3D diffraction vectors;(d_b) selecting a set L2_n of candidate second lattice plane normalvectors; (d_c) evaluating the fit of each candidate second lattice planenormal vector of the set L2_n with the subset SDV_n of 3D diffractionvectors; (d_d) selecting a subset SL2_n of the set L2_n comprising theN2 candidate second lattice plane normal vectors having the best fitwith the subset SDV_n of 3D diffraction vectors; (d_e) using said subsetSL2_n of candidate second lattice plane normal vectors to select asubset SSDV_n of the subset SDV_n of 3D diffraction vectors; (d_f)processing said subset SSDV_n of 3D diffraction vectors to determine acandidate unit cell CUC_n defined by three lattice vectors; (d_g)evaluating the fit of the candidate unit cell CUC n with the set DV of3D diffraction vectors; wherein the candidate unit cell of the N1candidate unit cells CUC_n (n=1 to N1) having the best fit with the fullset DV of 3D diffraction vectors is selected as the primary candidateunit cell PCUC.
 3. A method according to claim 2, wherein step (b)comprises the steps of: (b1) performing steps (b1_a) and (b_b) for eachj candidate first lattice plane normal vector of the set L1: (b1_a)projecting the set DV of 3D diffraction vectors onto said j candidatefirst lattice plane normal vector resulting in a set DV1D_j of projected1D diffraction vectors; (b1_b) filtering the set DV1D_j of projected 1Ddiffraction vectors with a plurality of filters comprising a regulararray of box functions, the width of the boxes being the same for allfilter but the distance ‘d’ between the boxes is increased from a valued_min to a value d_max, the output of each filtering process K is thenormalised number of projected 1D diffraction vectors that arepositioned within a box function, wherein the highest K value is used asa measure of the fit of the n'th candidate first lattice plane normalvector with the set DV of 3D diffraction vectors; and wherein step (c)comprises selecting the N1 candidate first lattice plane normal vectorshaving the highest K value.
 4. A method according to claim 2, whereinstep (c) further comprises for each n candidate first lattice planenormal vector of the subset SL1, optimizing the n candidate firstlattice plane normal vector by performing a local grid search resultingin an subset SL1* of optimized candidate first lattice plane normalvectors, wherein the optimized subset SL1* is used in step (d).
 5. Amethod according to claim 2, wherein step (d_c) comprises the steps of:(d_c1) performing steps (d_c1_a) and (d_c1_b) for each k candidatesecond lattice plane normal vector of the set L2_n: (d_c1_a) projectingthe subset SDV_n of 3D diffraction vectors onto said k candidate secondlattice plane normal vector resulting in a set SDV_n1D_n of projected 1Ddiffraction vectors; (d_c2_b) filtering the set SDV_n1D_k of projected1D diffraction vectors with a plurality of filters comprising a regulararray of box functions, the width of the boxes being the same for allfilter but the distance ‘d’ between the boxes is increased from a valued min to a value d_max, the output of each filtering process K is thenormalized number of projected 1D diffraction vectors that arepositioned within a box function, wherein the highest K value is used asa measure of the fit of the k'th candidate second lattice plane normalvector with the set SDV_n of 3D diffraction vectors; and wherein step(d_d) comprises selecting the N3 candidate second lattice plane normalvectors having the highest K value.
 6. A method according to claim 2,wherein step (d_d) further comprises for each k candidate second latticeplane normal vector of the subset SL2_n, optimizing the k candidatesecond lattice plane normal vector by performing a local grid searchresulting in an subset SL2_n* of optimized candidate second latticeplane normal vectors, wherein the optimized subset SL2_n* is used instep (d_e).
 7. A method according to claim 6, wherein the candidatesecond lattice plane normal vector CL2_n of the subset SL2_n* having thebest fit with the subset SDV_n of 3D diffraction vectors is used toselect the subset SSDV_n of the subset SDV_n of 3D diffraction vectors.8. A method according to claim 2, wherein step (d_e) comprises selectingthe 3D diffraction vectors of the subset SDV_n that when they areprojected onto CL2_n are positioned within a box function for the filterhaving the highest K value.
 9. A method according to claim 2, whereinstep (d_f) comprises the steps of: (d_f_a) selecting a subset SSSDV_n ofthe subset SSDV_n of 3D diffraction vectors; (d_f_b) determining thenormal vector for all planes spanned by three diffraction vectors of thesubset SSSDV_n of 3D diffraction vectors (ignoring sets of threediffraction vectors having a cross product close to zero) each normalvector being a candidate lattice plane normal vector thereby resultingin a set NL_n of new candidate lattice plane normal vectors; (d_f_c)evaluating the fit of each new candidate lattice plane normal vector ofthe set NL_5n with the subset SSDV_n of 3D diffraction vectors; and(d_f_d) determining a candidate unit cell CUC_n.
 10. A method accordingto claim 9, wherein step (d_f_c) comprises: (d_f_c1) performing steps(d_f_c1_a) and (d_f_c1_b) for each i new candidate lattice plane normalvector of the set NL_n: (d_f_c1_a) projecting the subset SSDV_n of 3Ddiffraction vectors onto said i new candidate lattice plane normalvector resulting in a set SSDV_n_1D_i of 1D diffraction vectors;(d_f_c1_b) filtering the set SSDV_n_1D_i of projected 1D diffractionvectors with a plurality of filters comprising a regular array of boxfunctions, the width of the boxes being the same for all filter but thedistance ‘d’ between the boxes is increased between a value d_min and avalue d_max, the output of each filtering process K is the number ofprojected 1D diffraction vectors that are positioned within a boxfunction, wherein the K value for the filter having the largest distance‘d*’ between the boxes while maintaining a K value being at least X % ofthe maximum K value for all filters is selected as a measure F of thefit of the i'th new candidate lattice plane normal vector with thesubset SSDV_n of 3D diffraction vectors, and wherein the vector pointingalong the i'th new candidate lattice plane normal vector and having thelength 1/‘d*’ is selected as a candidate direct-lattice vector whereby aset CDLV_n of candidate direct-lattice vectors is created, and whereinstep (d_f_d) comprises: selecting three linear independent candidatedirect-lattice vectors of the set CDLV_n based on their F values as thecandidate unit cell CUC_n.
 11. A method of indexing a plurality ofdiffraction spots, said method comprising the steps of: indexing thediffraction vectors corresponding to the plurality of diffraction spotsusing a method according to claim 1; and using said indexed diffractionvectors to index said corresponding diffraction spots.
 12. A method ofdetermining the composition of a poly-crystalline sample comprising thesteps of: illuminating a poly-crystalline sample with an X-ray source ata plurality of orientations and recording diffraction spots using a 2Dor 3D X-ray detector for each of said plurality of orientations;transforming said recorded diffraction spots into a set DV ofdiffraction vectors; indexing said set DV of diffraction vectors using amethod according to claim 1; and processing said indexed diffractionvectors to determine the composition of the polycrystal sample.
 13. Acomputer program product comprising program code means adapted to causea data processing system to perform the steps of the method according toclaim 1, when said program code means are executed on the dataprocessing system.
 14. A computer program product according to claim 13,wherein said computer program product comprises a non-transitorycomputer-readable medium having stored thereon the program code means.15. An X-ray diffraction apparatus for analysing a poly-crystallinesample, comprising: an X-ray source for emitting an X-ray beam; astaging device for positioning the poly-crystalline sample in the beampath in a plurality of mutually different angular positions relative tothe to the X-ray source; an X-ray detector for detecting diffractedX-ray beams leaving the poly-crystalline sample; and a processing unitoperationally connected to said X-ray detector said processing unitbeing configured to determine a set DV of 3D diffraction vectors byprocessing data received from said X-ray detector obtained byilluminating the poly-crystalline sample at a plurality of orientations;and indexing said set DV of 3D diffraction vectors using a methodaccording to claim 1.